Load the data

surveys_complete <- read.csv("data/surveys_complete.csv")

Example 1: Build the basic plot framework

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length))

Adding Geoms defines the geometry (lines, points, box) of the data output.

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
  geom_point()

### Another way of coding the same plot by assigning it to a variable and drawing it:

# Assign plot to a variable

surveys_plot <- ggplot(data = surveys_complete, 
                       mapping = aes (x = weight, y = hindfoot_length))

#Draw the plot

surveys_plot + 
  geom_point()

Pros and cons of hex bin plots

Hex bin plots are good for creating a visual “attention getter” for pulling attention towards a particular heavily-represented cluster of data, but you may lose some resolution. That is to say, not every data point is physically apparent on the plot, and so data may seem more “rigid” than it actually is.

Iterative modifying of the plot

The alpha variable modyfies transparency of the geometry. The color variable modifies the color of the geometry. You can also color each unique ID of the given points separately by adding (see example)

# First, define the base of the plot. Assign the dataset, and the x and y variables from the dataset, and the type of geometry.

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
    geom_point()

# Modify transparency with alpha
ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1)

#Color th epoints
ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, color = "blue")

#Color points by species ID

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, aes(color = species_id))

Boxplots

Can visualize distributions of a variable within another (such as distribution of weight within each species) Adding points with geom_jitter you can get a better idea of the number of points and their distributions. To bring the boxplot to the front, change the order of commands for jitter and boxplot.

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
  geom_boxplot()

# Add points to the boxplot. outlier.shape defines outlier points on the whiskers

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(alpha = 0.3, color = "tomato")

Plotting time series data

First, group the data and count records within each group. Timelapse data can be visualized with a line plot. You must tell ggplot to draw a separate line for each genus. You can also color code by genus.

# Count
yearly_counts <- surveys_complete %>% 
  count(year, genus)

# Make a basic line plot

ggplot(data = yearly_counts, aes(x = year, y = n)) +
  geom_line()

# Make a separate line for each genus

ggplot(data = yearly_counts, aes(x = year, y = n, group = genus)) +
  geom_line()

# Color code by genus 
ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
  geom_line()

Integrating the pipe operator with ggplot2

You can use the pipe operator to pass the data argument to ggplot. This is separate from the + operator used within ggplot itself. This can be useful to link data manipulation with visualization.

# Pass the yearly_counts dataset to ggplot

yearly_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = genus)) +
  geom_line()

# Link manipulation with visualization

yearly_counts_graph <- surveys_complete %>% 
  count(year, genus) %>% 
  ggplot(mapping = aes(x = year, y = n, color = genus)) +
  geom_line()

yearly_counts_graph

Faceting

Faceting is a technique that allows you to split one plot into multiple based on a factor within the dataset, such as generating a separate plot for each genus with one command. You can even split the plot by another factor (such as sex) so long as the data frame is grouped by that factor as well. You can facet by multiple factors at once, and organnize the panels in the facet.

# generate plots of genera counts over time
ggplot(data = yearly_counts, aes(x = year, y = n)) +
  geom_line() +
  facet_wrap(facets = vars(genus))

# generate a data frame grouped by year, genus, and sex

yearly_sex_counts <- surveys_complete %>% 
  count(year, genus, sex)

# Split the line of each plot into two lines; one for each sex

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(facets = vars(genus))

# Facet by both sex and genus

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(sex), cols = vars(genus))

# Organize the facet into one column, facet by rows

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(genus))

# Organize the facet into one row, faceted by columns

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(cols = vars(genus))

ggplot2 themes

Components of graphs can be customized using the theme function. There are pre-loaded themes available that change the overall appearance of the graph without much effort, or individual components can be singly changed.

# Apply the generic white background theme 

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(vars(genus)) +
  theme_bw()

Customization

Change the labels of axes and add a title with the labs function. Font can be changed in the theme function. If you like changes, you can save them to a custom theme to apply to other plots.

# Add labels and a title

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(vars(genus)) +
  labs(title = "Observed genera through time", 
       x = "Year of observation", 
       y = "Number of individuals") +
  theme_bw()

# Change the font size

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(vars(genus)) +
  labs(title = "Observed genera through time", 
       x = "Year of observation", 
       y = "Number of individuals") +
  theme_bw() +
  theme(text = element_text(size = 16))

# Flip the angle and orientation of labels

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 12),
                        strip.text = element_text(face = "italic"),
                        text = element_text(size = 16))

# Save changes to a theme

grey_theme <- theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 12),
                        strip.text = element_text(face = "italic"),
                        text = element_text(size = 16))

ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
  geom_boxplot() +
  grey_theme

Arranging Plots

To produce a single figure that contains multiple plots with different variables or data frames, the patchwork package can be used. In patchwork, use + to place plots next to each other, / to arrange them vertically, and plot_layout () to determine how much space each uses.

library(patchwork)

plot_weight <- ggplot(data = surveys_complete, aes(x = species_id, y = weight)) +
  geom_boxplot() +
  labs(x = "Species", y = expression(log[10](Weight))) +
  scale_y_log10()

plot_count <- ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
  geom_line() +
  labs(x = "Year", y = "Abundance")

plot_weight / plot_count + plot_layout(heights = c(3, 2))

Challenge 1

First, install the hexbin package, then use geom_hex function

library(hexbin)

surveys_plot +
  geom_hex()

Challenge 2

Create a scatter plot of weight over species_id with the plot types showing in different colors.

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
  geom_point(aes(color = plot_type))

Challenge 3

Boxplots are useful for summarizing, but hides the shape of the distribution. You can’t tell whether a distribution is bimodal or normal. An alternative is a violin plot. Change the data to a violin plot to see the distributions of the data, and then change the y scale to a log10 scale. Next, create a boxplot for hindfoot_length. Overlay the boxplot layer on a jitter layer to show actual measurements. Then, add color to the boxplot data points according to the plot_id

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
  geom_jitter(alpha = 0.3, color = "tomato") +
  geom_violin() 

# Change scale
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
  scale_y_log10() +
  geom_jitter(alpha = 0.3, color = "tomato") +
  geom_violin() 

# Overlay a boxplot of hindfoor_length

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = hindfoot_length)) +
  geom_jitter(alpha = 0.3, color = "tomato") +
  geom_boxplot(outlier.shape = NA) 

# Add color according to plot_id

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
  geom_jitter(alpha = 0.3, color = "tomato") +
  geom_boxplot(outlier.shape = NA, aes(color = as.factor(plot_id)))

# Challenge 4

Create a plot that depicts how the average weight of each species changes through the years.

yearly_weight <- surveys_complete %>% 
  group_by(year, species_id) %>% 
  summarise(avg_weight = mean(weight))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
ggplot(data = yearly_weight, mapping = aes(x = year, y = avg_weight)) +
  geom_line() +
  facet_wrap(vars(species_id)) +
  theme_bw()

# Chalenge 5

Improve one of the plots generated in this exercise

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
  geom_jitter(alpha = 0.3, color = "purple") +
  geom_boxplot(outlier.shape = NA, aes(color = as.factor(plot_id))) +
  theme(axis.text.x = element_text(colour = "grey3", size = 8),  
        axis.text.y = element_text(colour = "grey3", size = 8)) +
    labs(title = "Recorded weight of specimens",
        x = "Species",
        y = "Weight (g)") +
  guides(color = guide_legend(title = "Plot ID"))